import os
from IPython.display import display, HTML, IFrame
NBdir='C:\\Users\\Gamaliel\\Documents\\G\\ADD\\IBM_DS\\Visualization\\'
os.chdir(NBdir)

ModuleFolder='C:\\Users\\Gamaliel\\Documents\\G\\ADD\\IBM_DS\\Visualization\\M01\\IBMs\\HTML\\'
os.chdir(ModuleFolder)
for file in os.listdir(ModuleFolder):
    #if file.lower().endswith('HTMLs.html'):
        #continue
    if file.lower().endswith('.html'):
        link=file
        #print(link)  
        display(HTML(ModuleFolder+link))
    elif file.lower().endswith('.htm'):
        link=file
        #print(link)  
        display(HTML(ModuleFolder+link))
    else:
        continue

Data Visualization with Python

Cheat Sheet : Data Preprocessing Tasks in Pandas

Task	Syntax	Description	Example
Load CSV data	`pd.read_csv('filename.csv')`	Read data from a CSV file into a Pandas DataFrame	`df_can=pd.read_csv('data.csv')`
Handling Missing Values	`df.dropna()`	Drop rows with missing values	`df_can.dropna()`
	`df.fillna(value)`	Fill missing values with a specified value	`df_can.fillna(0)`
Removing Duplicates	`df.drop_duplicates()`	Remove duplicate rows	`df_can.drop_duplicates()`
Renaming Columns	`df.rename(columns={'old_name': 'new_name'})`	Rename one or more columns	`df_can.rename(columns={'Age': 'Years'})`
Selecting Columns	`df['column_name']` or `df.column_name`	Select a single column	`df_can.Age` or `df_can['Age]'`
	`df[['col1', 'col2']]`	Select multiple columns	`df_can[['Name', 'Age']]`
Filtering Rows	`df[df['column'] > value]`	Filter rows based on a condition	`df_can[df_can['Age'] > 30]`
Applying Functions to Columns	`df['column'].apply(function_name)`	Apply a function to transform values in a column	`df_can['Age'].apply(lambda x: x + 1)`
Creating New Columns	`df['new_column'] = expression`	Create a new column with values derived from existing ones	`df_can['Total'] = df_can['Quantity'] * df_can['Price']`
Grouping and Aggregating	`df.groupby('column').agg({'col1': 'sum', 'col2': 'mean'})`	Group rows by a column and apply aggregate functions	`df_can.groupby('Category').agg({'Total': 'mean'})`
Sorting Rows	`df.sort_values('column', ascending=True/False)`	Sort rows based on a column	`df_can.sort_values('Date', ascending=True)`
Displaying First n Rows	`df.head(n)`	Show the first n rows of the DataFrame	`df_can.head(3)`
Displaying Last n Rows	`df.tail(n)`	Show the last n rows of the DataFrame	`df_can.tail(3)`
Checking for Null Values	`df.isnull()`	Check for null values in the DataFrame	`df_can.isnull()`
Selecting Rows by Index	`df.iloc[index]`	Select rows based on integer index	`df_can.iloc[3]`
	`df.iloc[start:end]`	Select rows in a specified range	`df_can.iloc[2:5]`
Selecting Rows by Label	`df.loc[label]`	Select rows based on label/index name	`df_can.loc['Label']`
	`df.loc[start:end]`	Select rows in a specified label/index range	`df_can.loc['Age':'Quantity']`
Summary Statistics	`df.describe()`	Generates descriptive statistics for numerical columns	`df_can.describe()`

Cheat Sheet : Plot Libraries

Library	Main Purpose	Key Features	Programming Language	Level of Customization	Dashboard Capabilities	Types of Plots Possible
Matplotlib	General-purpose plotting	Comprehensive plot types and variety of customization options	Python	High	Requires additional components and customization	Line plots, scatter plots, bar charts, histograms, pie charts, box plots, heatmaps, etc.
Pandas	Fundamentally used for data manipulation but also has plotting functionality	Easy to plot directly on Panda data structures	Python	Medium	Can be combined with web frameworks for creating dashboards	Line plots, scatter plots, bar charts, histograms, pie charts, box plots, etc.
Seaborn	Statistical data visualization	Stylish, specialized statistical plot types	Python	Medium	Can be combined with other libraries to display plots on dashboards	Heatmaps, violin plots, scatter plots, bar plots, count plots, etc.
Plotly	Interactive data visualization	interactive web-based visualizations	Python, R, JavaScript	High	Dash framework is dedicated for building interactive dashboards	Line plots, scatter plots, bar charts, pie charts, 3D plots, choropleth maps, etc.
Folium	Geospatial data visualization	Interactive, customizable maps	Python	Medium	For incorporating maps into dashboards, it can be integrated with other frameworks/libraries	Choropleth maps, point maps, heatmaps, etc.
PyWaffle	Plotting Waffle charts	Waffle charts	Python	Low	Can be combined with other libraries to display waffle chart on dashboards	Waffle charts, square pie charts, donut charts, etc.

ModuleFolder='C:\\Users\\Gamaliel\\Documents\\G\\ADD\\IBM_DS\\Visualization\\M02\\IBMs\\HTMLs\\'
os.chdir(ModuleFolder)
for file in os.listdir(ModuleFolder):
    #if file.lower().endswith('HTMLs.html'):
        #continue
    if file.lower().endswith('.html'):
        link=file
        #print(link)  
        display(HTML(ModuleFolder+link))
    elif file.lower().endswith('.htm'):
        link=file
        #print(link)  
        display(HTML(ModuleFolder+link))
    else:
        continue

Understanding Treemaps and Pivot Charts

Treemaps

Treemaps are a form of data visualization that displays hierarchical data using nested rectangles. Each branch of the hierarchy is given a rectangle, which is then tiled with smaller rectangles representing sub-branches. Treemaps are particularly useful for visualizing large datasets where the hierarchical structure is crucial, offering an intuitive and space-efficient way to display data.

Applications of Treemaps

Treemaps are employed across various domains due to their ability to effectively communicate complex hierarchical data. Some common applications include:

Business Analytics: Visualizing the composition of sales by product categories and subcategories.
Finance: Displaying the performance of stock portfolios, sectors, and industries.
IT and Network Management: Representing file systems or network usage, showing the distribution of files and folders.
Bioinformatics: Displaying hierarchical biological data, such as taxonomies or genomic structures.
Website Analytics: Showing the structure of website traffic, with rectangles representing web pages and their size indicating the volume of visits.

Importance in Data Visualization

Treemaps are important in data visualization for several reasons:

Space Efficiency: Treemaps make efficient use of space, allowing large datasets to be visualized within a limited area.
Hierarchy Representation: They provide a clear representation of hierarchical data, showing both the structure and the quantitative relationship between elements.
Comparative Analysis: Treemaps make it easy to compare the sizes of different elements at various levels of the hierarchy.
Immediate Insight: The color and size of the rectangles can quickly convey important information, making it easier for users to spot patterns and outliers.

Syntax for Generating Treemap

We can generate Treemaps using the Plotly library in Python.

Install Required Libraries:

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pip install plotly pandas

Import Libraries:

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import pandas as pd
import plotly.express as px

Load Data:

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# Replace with your actual dataset or data source
data = {
    'Category': ['Category 1', 'Category 1', 'Category 2', 'Category 2', 'Category 3'],
    'Subcategory': ['Subcategory 1A', 'Subcategory 1B', 'Subcategory 2A', 'Subcategory 2B', 'Subcategory 3A'],
    'Value': [10, 20, 30, 40, 50]
}
df = pd.DataFrame(data)

Create Treemap:

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fig = px.treemap(df,
                 path=['Category', 'Subcategory'],  # Define hierarchical structure
                 values='Value',  # Size of each rectangle
                 title='Treemap Example')  # Title of the treemap

Show Treemap:

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fig.show()

Practical Example: Visualizing Sales Data

Let's use Plotly Express to visualize the sales data example.

Sample Data

Assume we have the following hierarchical sales data:

Electronics
- Laptops: 120,000
- Smartphones: 80,000
- Tablets: 30,000
Furniture
- Chairs: 50,000
- Tables: 40,000
- Sofas: 20,000
Clothing
- Men: 70,000
- Women: 90,000
- Kids: 40,000

Code to Generate Treemap

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import plotly.express as px
import pandas as pd
# Sales data
data = {
    'Category': ['Electronics', 'Electronics', 'Electronics', 
                 'Furniture', 'Furniture', 'Furniture', 
                 'Clothing', 'Clothing', 'Clothing'],
    'Subcategory': ['Laptops', 'Smartphones', 'Tablets', 
                    'Chairs', 'Tables', 'Sofas', 
                    'Men', 'Women', 'Kids'],
    'Sales': [120000, 80000, 30000, 
              50000, 40000, 20000, 
              70000, 90000, 40000]
}
df = pd.DataFrame(data)
# Creating the treemap
fig = px.treemap(
    df,
    path=['Category', 'Subcategory'],
    values='Sales',
    title='Sales Data Treemap'
)
fig.show()

Output

Conclusion

Treemaps are a powerful tool for visualizing hierarchical data, offering an efficient and intuitive way to compare elements within a hierarchy. Their applications are diverse, ranging from business analytics to bioinformatics. By following the provided code, you can generate treemaps to visualize your own hierarchical datasets, making it easier to gain insights and communicate information effectively.

Pivot Charts

Introduction

Pivot charts are a powerful tool used for data visualization and analysis. They allow users to dynamically summarize and explore large datasets, revealing insights and trends that might not be immediately obvious. Pivot charts are widely used in business intelligence, finance, marketing, and various other fields where data analysis is crucial.

Applications of Pivot Charts

Business Intelligence: Pivot charts help in summarizing complex business data, making it easier for stakeholders to make informed decisions.
Finance: Analysts use pivot charts to visualize financial data, track performance metrics, and forecast trends.
Marketing: Marketers leverage pivot charts to analyze campaign performance, customer demographics, and sales trends.
Operations: Operational managers use pivot charts to monitor supply chain performance, inventory levels, and process efficiencies.
Healthcare: Pivot charts assist in visualizing patient data, treatment outcomes, and operational efficiency in healthcare settings.

Importance in Data Visualization

Data Summarization: Quickly summarizes large datasets, making them more manageable and understandable.
Dynamic Analysis: Allows users to interactively explore data by filtering, sorting, and drilling down into specific areas of interest.
Trend Identification: Helps in identifying patterns and trends that can inform strategic decision-making.
Efficiency: Enhances productivity by providing a quick way to visualize and interpret data without extensive manual processing.
Presentation: Facilitates the creation of professional and informative reports that can be easily shared with stakeholders.

Sample

Consider a sample data given below. This data is assumed to have a 100 entries.

Item	Category	Subcategory	Value
P	B	Y	62
S	C	Y	86
Q	A	X	98
P	A	Y	45
S	C	X	35
…	…	…	…
P	B	X	37
R	B	Y	26
S	A	Y	33
Q	A	Y	43
P	A	Y	15

Treating the column Item as the index, i.e., the row component, and Category and Subcategory as columns, i.e., the column components, with Value acting as the aggregated entity, the pivot graph can be created, summarizing the data as shown below.

Syntax for Generating Pivot Charts in Python

Python, with libraries such as Pandas and Matplotlib, provides robust capabilities for creating pivot charts. Below is a step-by-step guide to generating pivot charts using Python.

Install Required Libraries:

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pip install pandas matplotlib

Import Libraries:

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import pandas as pd
import matplotlib.pyplot as plt

Load Data:

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# Load data into a pandas DataFrame
data = pd.read_csv('your_dataset.csv')

Create a Pivot Table:

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pivot_table = data.pivot_table(values='ValueColumn', index='RowIndexColumn', columns='ColumnIndexColumn', aggfunc='sum')

Generate Pivot Chart:

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pivot_table.plot(kind='bar')
plt.title('Pivot Chart Title')
plt.xlabel('X-axis Label')
plt.ylabel('Y-axis Label')
plt.show()

Practical Example

Let's walk through a practical example using a sample dataset. We'll create a pivot chart to visualize sales data.

Sample Data:
For this example, we are creating dummy data on sales of IT products across different quarters. The data generated would be of the following form.

S. No.	Date	Category	Subcategory	Sales
0	Q1	Peripherals	Accessories	2092
1	Q1	Software	Accessories	4695
2	Q1	Software	Components	3106
3	Q1	Desktops	Accessories	3527
4	Q1	Laptops	Software Suites	1182
…	…	…	…	…
2395	Q4	Desktops	Accessories	2557
2396	Q4	Software	Accessories	2626
2397	Q4	Desktops	Components	2427
2398	Q4	Software	Components	1768
2399	Q4	Peripherals	Components	1714

Click here to get the code

This data was generated using the code below.

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import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
# Define the parameters
num_quarters = 4
num_categories = 4
num_subcategories = 3
num_samples = num_quarters * num_categories * num_subcategories*50
# Generate sample sales data
np.random.seed(40)
# Creating sample data
dates = np.repeat(['Q1','Q2','Q3','Q4'], 50*num_categories * num_subcategories)
categories = np.tile(np.random.choice(['Laptops', 'Desktops', 'Peripherals', 'Software'], size=num_quarters*50), num_categories * num_subcategories)
subcategories = np.tile(np.random.choice(['Accessories', 'Components', 'Software Suites'], size=num_quarters*50), num_categories * num_subcategories)
sales_values = np.random.randint(1000, 5000, size=num_samples)
# Create DataFrame
df = pd.DataFrame({
    'Date': dates,
    'Category': categories,
    'Subcategory': subcategories,
    'Sales': sales_values
})

Create a Pivot Table:

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# Create pivot table
pivot_table = df.pivot_table(index='Date', columns=['Category','Subcategory'], values='Sales', aggfunc=np.sum)

Generate Pivot Chart:

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# Plotting a pivot chart
pivot_table.plot(kind='bar', figsize=(14, 8))
plt.title('Sales Summary of IT Products by Category and Subcategory')
plt.xlabel('Quarters')
plt.ylabel('Total Sales')
plt.grid(False)
plt.legend(title=('Category', 'Subcategory'), bbox_to_anchor=(1.05, 1), loc='upper left')
plt.tight_layout()
plt.show()

Output

Conclusion

Pivot charts are an indispensable tool for data analysis, providing a dynamic and intuitive way to summarize and visualize data. By leveraging libraries like Pandas and Matplotlib in Python, you can easily create pivot charts to uncover insights and make data-driven decisions. Whether for business intelligence, finance, marketing, or any other field, pivot charts enhance the ability to analyze and interpret data effectively.

Data Visualization with Python

Cheat Sheet : Plotting with Matplotlib using Pandas

Plot Type	Description	Pandas Function	Example
Line Plot	Shows trends and changes over time	`DataFrame.plot.line()` `DataFrame.plot(kind = ‘line’)`	`df.plot(x=’year’, y=’sales’, kind=’line’)`
Area Plot	Displays data series as filled areas, showing the relationship between them	`DataFrame.plot.area()` `DataFrame.plot(kind = ‘area’)`	`df.plot(kind='area')`
Histogram	Displays bars representing the data count in each interval/bin	`Series.plot.hist()` `Series.plot(kind = ‘hist’, bins = n)`	`s.plot(kind='hist', bins=10)` `df[‘age’].plot(kind='hist', bins=10)`
Bar Chart	Displays data using rectangular bars	`DataFrame.plot.bar()` `DataFrame.plot(kind = ‘bar’)`	`df.plot(kind='bar')`
Pie Chart	Displays data as a circular plot divided into slices, representing proportions or percentages of a whole	`Series.plot.pie()` `Series.plot(kind = ‘pie’)` `DataFrame.plot.pie(y, labels)` `DataFrame.plot(kind = ‘pie’)`	`s.plot(kind='pie’,autopct='%1.1f%%')` `df.plot(x='Category',y='Percentage',kind='pie')`
Box Plot	Displays the distribution of a dataset along with key statistical measures	`DataFrame.plot.box()` `DataFrame.plot(kind = ‘box’)`	`df_can.plot(kind='box')`
Scatter Plot	Uses Cartesian coordinates to display values for two variables	`DataFrame.plot.scatter()` `DataFrame.plot(x, y, kind = ‘scatter’)`	`df.plot(x='Height', y='Weight', kind='scatter')`

Cheat Sheet : Plotting directly with Matplotlib

Plot Type	Description	Matplotlib Function	Example
Line Plot	Shows trends and changes over time	`plt.plot()`	`plt.plot(x, y, color='red', linewidth=2)`
Area Plot	Display data series as filled areas	`plt.fill_between()`	`plt.fill_between(x, y1, y2, color='blue', alpha=0.5)`
Histogram	Displays bars representing the data count in each interval/bin	`plt.hist()`	`plt.hist(data, bins=10, color='orange', edgecolor='black')`
Bar Chart	Displays data using rectangular bars	`plt.bar()`	`plt.bar(x, height, color='green', width=0.5)`
Pie Chart	Displays data as a circular plot divided into slices, representing proportions or percentages of a whole	`plt.pie()`	`plt.pie(sizes, labels=labels, colors=colors, explode=explode)`
Box Plot	Displays the distribution of a dataset along with key statistical measures	`plt.boxplot()`	`plt.boxplot(data, notch=True)`
Scatter Plot	Uses Cartesian coordinates to display values for two variables	`plt.scatter()`	`plt.scatter(x, y, color='purple', marker='o', s=50)`
Subplotting	Creating multiple plots on one figure	`plt.subplots()`	`fig, axes = plt.subplots(nrows=2, ncols=2)`
Customization	Customizing plot: adding labels, title, legend, grid	Various customization	`plt.title('Title')` `plt.xlabel('X Label')` `plt.ylabel('Y Label')` `plt.legend()` `plt.grid(True)`

Author(s)

Dr. Pooja

Data Visualization with Python

Cheat Sheet : Maps, Waffles, WordCloud and Seaborn

Function	Description	Syntax	Example
Folium
Map	Create a map object with specified center coordinates and zoom level.	`folium.Map(location=[lat, lon], zoom_start=n)`	`world_map = folium.Map()` `canada =folium.Map(location=[56.130, -106.35], zoom_start=4)`
Marker	Add a marker to the map with custom icon, popup, and tiles Tiles as Stamen Toner	`folium.Marker(location=[lat , lon ],` `popup='Marker Popup',` `tiles='Stamen Toner').add_to(map)`	`folium.Marker(location=[556.130, -106.35],` `tooltip='Marker',` `tiles='Stamen Toner').add_to(world_map)`
	Tiles as Stamen Terrain	`folium.Marker(location=[lat , lon ],` `popup='Marker Popup',` `tiles='Stamen Terrain').add_to(map)`	`folium.Marker(location=[556.130, -106.35],` `tooltip='Marker',` `tiles='Stamen Terrain').add_to(world_map)`
Circle	Add a circle to the map with specified radius, color, and fill opacity.	`folium.features.CircleMarker(location=[lat, lon],` `radius=n, color='red',` `fill_opacity=n).add_to(map)`	`folium.features.CircleMarker(location=[56.130, -106.35],` `radius=1000, color='red',` `fill_opacity=0.5).add_to(world_map)`
Chorpleth	Create a choropleth map based on a GeoJSON file and a specified data column.	`folium.Choropleth(geo_data='path/to/geojson_file',` `data=df, columns=['region', 'value_column'],` `key_on='feature.properties.id', fill_color='YlGnBu',` `fill_opacity=0.7, line_opacity=0.2,` `legend_name='Legend').add_to(map)`	`world_map.choropleth(geo_data=world_geo,` `data=df_can, columns=['Country', 'Total'],` `key_on='feature.properties.name', fill_color='YlOrRd',` `fill_opacity=0.7,line_opacity=0.2,` `legend_name='Immigration to Canada')`
PyWaffle
Waffle	Create a waffle chart based on values and categories.	`plt.figure(FigureClass = Waffle,rows = 20, columns = 30, values = values)` `waffle_chart = waffle.Waffle(values=[value1, value2, ...],` `rows=n, columns=n)`	`plt.figure(FigureClass = Waffle,rows = 20, columns = 30,` `values = df_dsn['Total'], cmap_name = 'tab20',` `legend = {'labels': label,'loc': 'lower left',` `'bbox_to_anchor':(0,-0.1),'ncol': 3})`
Legend	Add a legend to the waffle chart.	`waffle_chart.legend(loc='upper left', bbox_to_anchor=(1, 1))`
Title	Add a title to the waffle chart.	`waffle_chart.set_title('Waffle Chart Title')`
Labels	Add labels to the waffle chart.	`waffle_chart.set_labels(['Label 1', 'Label 2', ...])`
WordCloud
WordCloud	Create a word cloud object based on text data.	`wordcloud = WordCloud().generate(text_data)`	`alice_wc = WordCloud(background_color='white', max_words=2000, mask=alice_mask, stopwords=stopwords)` `alice_wc.generate(alice_novel)` `plt.imshow(alice_wc, interpolation='bilinear')`
Generate	Generate the word cloud based on the text data.	`wordcloud.generate(text_data)`
Display	Display the word cloud using matplotlib or other plotting libraries.	`plt.imshow(wordcloud, interpolation='bilinear')`
Options	Set various options for the word cloud, such as font, colors, mask, and stopwords.	`wordcloud = WordCloud(font_path='path/to/font_file',` `background_color='white',` `colormap='Blues', mask=mask_image,` `stopwords=stopwords).generate(text_data)`
Seaborn
barplot	Create a bar plot to visualize the relationship between a categorical variable and a numeric variable.	`sns.barplot(x='x_variable', y='y_variable', data=dataframe)`	`sns.barplot(x='Continent', y='Total', data=df_can1)`
countplot	Create a count plot to display the frequency of each category in a categorical variable.	`sns.countplot(x='category', data=dataframe)`	`sns.countplot(x='Continent', data=df_can)`
regplot	Create a scatter plot with a linear regression line to visualize the relationship between two numeric variables.	`sns.regplot(x='x_variable',` `y='y_variable', data=dataframe)`	`sns.regplot(x='year', y='total', data=df_tot)`

Author(s)

Dr. Pooja

Changelog

Date	Version	Changed by	Change Description
2023-06-18	0.1	Dr. Pooja	Initial version created

ModuleFolder='C:\\Users\\Gamaliel\\Documents\\G\\ADD\\IBM_DS\\Visualization\\M04\\HTMLs\\'
os.chdir(ModuleFolder)
for file in os.listdir(ModuleFolder):
    #if file.lower().endswith('HTMLs.html'):
        #continue
    if file.lower().endswith('.html'):
        link=file
        #print(link)  
        display(HTML(ModuleFolder+link))
    elif file.lower().endswith('.htm'):
        link=file
        #print(link)  
        display(HTML(ModuleFolder+link))
    else:
        continue

os.chdir(NBdir)
try:
    !jupyter nbconvert Vis_HTMLs.ipynb --to html --template pj
except Exception as e:
    print('HTML not stored')

import shutil
import os
import shutil
FromFld=NBdir
Tofld='C:\\Users\\Gamaliel\\Documents\\G\\ADD\\IBM_DS\\IBM_DS_Jupyter_Tasks\\Python4DataScience\\'
HTML_Notes='Vis_HTMLs.html'
Jupyter_Notes='Vis_HTMLs.ipynb'
try:
    if os.path.isfile(Tofld+'/'+HTML_Notes):
        os.remove(Tofld+'/'+HTML_Notes)
        print(HTML_Notes, 'deleted in', Tofld)
        shutil.move(os.path.join(FromFld,HTML_Notes),os.path.join(Tofld,HTML_Notes))
        print(HTML_Notes, 'replaced in', Tofld)
    else:
        shutil.move(os.path.join(FromFld,HTML_Notes),os.path.join(Tofld,HTML_Notes))
        print(HTML_Notes, 'written in', Tofld)
except Exception as e:
    print('HTML not moved')

    # NB

try:
    if os.path.isfile(Tofld+'/'+Jupyter_Notes):
        os.remove(Tofld+'/'+Jupyter_Notes)
        print(Jupyter_Notes, 'deleted in', Tofld)
        shutil.copy(os.path.join(FromFld,Jupyter_Notes),os.path.join(Tofld,Jupyter_Notes))
        print(Jupyter_Notes, 'copied in', Tofld)
    else:
        shutil.copy(os.path.join(FromFld,Jupyter_Notes),os.path.join(Tofld,Jupyter_Notes))
        print(Jupyter_Notes, 'copied in', Tofld)
except Exception as e:
    print('NB not moved')

Visualization notes

Gamaliel Mendoza

2025-06-09 12:26:41

Data Visualization with Python

Cheat Sheet : Data Preprocessing Tasks in Pandas

Cheat Sheet : Plot Libraries

Understanding Treemaps and Pivot Charts

Treemaps

Applications of Treemaps

Importance in Data Visualization

Syntax for Generating Treemap

Practical Example: Visualizing Sales Data

Sample Data

Code to Generate Treemap

Output

Conclusion

Pivot Charts

Introduction

Applications of Pivot Charts

Importance in Data Visualization

Sample

Syntax for Generating Pivot Charts in Python

Practical Example

Output

Conclusion

Data Visualization with Python

Cheat Sheet : Plotting with Matplotlib using Pandas

Cheat Sheet : Plotting directly with Matplotlib

Author(s)

Data Visualization with Python

Cheat Sheet : Maps, Waffles, WordCloud and Seaborn

Function

Description

Syntax

Example

Visual

Author(s)

Changelog

Item	Category	Subcategory	Value
P	B	Y	62
S	C	Y	86
Q	A	X	98
P	A	Y	45
S	C	X	35
…	…	…	…
P	B	X	37
R	B	Y	26
S	A	Y	33
Q	A	Y	43
P	A	Y	15

Item	Category	Subcategory	Value
P	B	Y	62
S	C	Y	86
Q	A	X	98
P	A	Y	45
S	C	X	35
…	…	…	…
P	B	X	37
R	B	Y	26
S	A	Y	33
Q	A	Y	43
P	A	Y	15

Item	Category	Subcategory	Value
P	B	Y	62
S	C	Y	86
Q	A	X	98
P	A	Y	45
S	C	X	35
…	…	…	…
P	B	X	37
R	B	Y	26
S	A	Y	33
Q	A	Y	43
P	A	Y	15